FG-GMM-Based Interactive Behavior Estimation for Autonomous Driving Vehicles in Ramp Merging Control

Abstract

Interactive behavior is important for autonomous driving vehicles, especially for scenarios like ramp merging which require significant social interaction between autonomous driving vehicles and human-driven cars. This paper enhances our previous Probabilistic Graphical Model (PGM) merging control model for the interactive behavior of autonomous driving vehicles. To better estimate the interactive behavior for autonomous driving cars, a Factor Graph (FG) is used to describe the dependency among observations and estimate other cars’ intentions. Real trajectories are used to approximate the model instead of human-designed models or cost functions. Forgetting factors and a Gaussian Mixture Model (GMM) are also applied in the intention estimation process for stabilization, interpolation and smoothness. The advantage of the factor graph is that the relationship between its nodes can be described by self-defined functions, instead of probabilistic relationships as in PGM, giving more flexibility. Continuity of GMM also provides higher accuracy than the previous discrete speed transition model. The proposed method enhances the overall performance of intention estimation, in terms of collision rate and average distance between cars after merging, which means it is safer and more efficient.